My work focuses on developing new methods for analyzing massive, spatiotemporal network data, and on using these data to better understand the economic lives of the poor. Most of this work is based in developing and conflict-affected countries. I have open research positions for qualified PhD students.

Recent Projects

We present the results of a field experiment in Afghanistan that was designed to increase adoption of mobile money, and determine if such adoption led to measurable changes in the lives of the adopters. The intervention we evaluate is a mobile salary payment program, in which a random subset of individuals of a large firm were transitioned into receiving their regular salaries in mobile money rather than in cash. While mobile money salaries led to immediate and significant cost savings to the employer, we find little consistent evidence that mobile money had an impact on several key indicators of individual wealth or well-being. Taken together, these results suggest that while mobile salary payments may greatly increase the efficiency and transparency of traditional economies, in the short run the benefits may be realized by those making the payments, rather than by those receiving them.

New sources of large-scale geospatial data can inform policy decisions ranging from disease monitoring and city planning to disaster management and humanitarian relief. However, existing methods for mining these data are not well suited to most developing country contexts where technology use is less intense and the digital traces are generally quite sparse. Here, we present a method for predicting the approximate location of a mobile phone subscriber that is more appropriate to contexts where the signal generated by each individual may be intermittent, but the collective population generates a large amount of data. This method works well when, for instance, an individual is not consistently active on the network or when the phone is off. Our model uses a nonparametric approach to probabilistically interpolate locations, and has the advantage of associating a confidence with each prediction. We test this method on a large dataset of anonymized mobile phone records from Afghanistan, and find that we can correctly predict a subscriber's unknown location in 76%-95% of cases, and that on average our predicted location is off by 0.2-1.9 kilometers.

We provide empirical evidence that an early form of "mobile money" is used to share risk. Our analysis is based on the entire universe of mobile phone-based communications over a four-year period in Rwanda, including millions of interpersonal transfers sent over the mobile phone network. Exploiting the quasi-random timing and location of natural disasters, we show that people make transfers to individuals affected by economic shocks. The magnitude of these transfers is small in absolute terms, but statistically strong. Unlike other documented forms of risk sharing, the mobile-phone based transfers are sent over large geographic distances and in response to covariate shocks. Transfers are more likely to be sent to wealthy individuals, and are sent predominantly between pairs of individuals with a strong history of reciprocal exchange. [View Video]

We describe how large sources of geotagged data generated by mobile phones can provide fine-grained insight into internal migration. We develop and formalize the concept of inferred mobility, and compute this and other metrics on a large dataset containing the phone records of 1.5 million Rwandans over four years. Our empirical results corroborate the findings of a recent government survey that notes relatively low levels of permanent migration in Rwanda. [View Video]

In Progress

Private firms in conflict-affected countries face insecurity, corruption, poor infrastructure, and weak property rights. Disbursing employee wages is a challenge as cash-based payment systems are vulnerable to indirect costs in the form of leakage and theft. We implement a randomized field experiment in Afghanistan to test the effects of a mobile phone-based salary payment system on performance outcomes in a private firm with approximately 400 employees.

Automatic payroll deductions consistently represent one of the most effective means of increasing savings in developed countries. We design and experimentally evaluate a mobile phone-based account that allows savings to be automatically deducted from salaries in Afghanistan, a country with extremely low levels of formal financial inclusion. We find that employees who are automatically enrolled in a defined-contribution account are 40 percentage points more likely to contribute to the account than individuals with a default contribution of zero. We also randomize employer matching contributions and find that the effect of automatic enrollment on participation is approximately equivalent to providing financial incentives equal to a 50 percent match

Freedom to Speak: How a Free Calling Network Affects Community Health Worker Knowledge and Productivity

We study the extent to which increased peer communication can improve the effectiveness of community health workers in Tanzania. Through a large field experiment in which roughly 8,000 health workers receive staggered access to a free mobile phone network, we measure the impact of this intervention on actual patterns of interaction and on health and welfare outcomes of workers and patients.

A Society of Silent Separation: The Impact of Migration on Ethnic Segregation in Estonia - joint with Ott Toomet (Tartu University)

We exploit a novel source of data to model the impact of migration and urbanization on segregation in Estonia. Analyzing the complete mobile phone records of hundreds of thousands of Estonians, we find that the ethnic composition of an individual's geographic neighborhood heavily influences the structure of the individual's phone-based network. We further find that patterns of segregation are significantly different for migrants than for the at-large population: migrants are more likely to interact with coethnics than non-migrants, but are less sensitive to the ethnic composition of their immediate neighborhood than non-migrants.